Using time-series chest radiographs and laboratory data by machine learning for identifying pulmonary infection and colonization of Acinetobacter baumannii.

BACKGROUND: Accurately distinguishing between pulmonary infection and colonization in patients with Acinetobacter baumannii is of utmost importance to optimize treatment and prevent antibiotic abuse or inadequate therapy. An efficient automated sorting tool could prompt individualized interventions and enhance overall patient outcomes. This study aims to develop a robust machine learning classification model using a combination of time-series chest radiographs and laboratory data to accurately classify pulmonary status caused by Acinetobacter baumannii. METHODS: We proposed nested logistic regression models based on different time-series data to automatically classify the pulmonary status of patients with Acinetobacter baumannii. Advanced features were extracted from the time-series data of hospitalized patients, encompassing dynamic pneumonia indicators observed on chest radiographs and laboratory indicator values recorded at three specific time points. RESULTS: Data of 152 patients with Acinetobacter baumannii cultured from sputum or alveolar lavage fluid were retrospectively analyzed. Our model with multiple time-series data demonstrated a higher performance of AUC (0.850, with a 95% confidence interval of [0.638-0.873]), an accuracy of 0.761, a sensitivity of 0.833. The model, which only incorporated a single time point feature, achieved an AUC of 0.741. The influential model variables included difference in the chest radiograph pneumonia score. CONCLUSION: Dynamic assessment of time-series chest radiographs and laboratory data using machine learning allowed for accurate classification of colonization and infection with Acinetobacter baumannii. This demonstrates the potential to help clinicians provide individualized treatment through early detection.

Time-Series MR Images Identifying Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using a Deep Learning Approach.

BACKGROUND: Pathological complete response (pCR) is an essential criterion for adjusting follow-up treatment plans for patients with breast cancer (BC). The value of the visual geometry group and long short-term memory (VGG-LSTM) network using time-series dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for pCR identification in BC is unclear. PURPOSE: To identify pCR to neoadjuvant chemotherapy (NAC) using deep learning (DL) models based on the VGG-LSTM network. STUDY TYPE: Retrospective. POPULATION: Center A: 235 patients (47.7 ± 10.0 years) were divided 7:3 into training (n = 164) and validation set (n = 71). Center B: 150 patients (48.5 ± 10.4 years) were used as test set. FIELD STRENGTH/SEQUENCE: 3-T, T2-weighted spin-echo sequence imaging, and gradient echo DCE sequence imaging. ASSESSMENT: Patients underwent MRI examinations at three sequential time points: pretreatment, after three cycles of treatment, and prior to surgery, with tumor regions of interest manually delineated. Histopathology was the gold standard. We used VGG-LSTM network to establish seven DL models using time-series DCE-MR images: pre-NAC images (t0 model), early NAC images (t1 model), post-NAC images (t2 model), pre-NAC and early NAC images (t0 + t1 model), pre-NAC and post-NAC images (t0 + t2 model), pre-NAC, early NAC and post-NAC images (t0 + t1 + t2 model), and the optimal model combined with the clinical features and imaging features (combined model). The models were trained and optimized on the training and validation set, and tested on the test set. STATISTICAL TESTS: The DeLong, Student's t-test, Mann-Whitney U, Chi-squared, Fisher's exact, Hosmer-Lemeshow tests, decision curve analysis, and receiver operating characteristics analysis were performed. P < 0.05 was considered significant. RESULTS: Compared with the other six models, the combined model achieved the best performance in the test set yielding an AUC of 0.927. DATA CONCLUSION: The combined model that used time-series DCE-MR images, clinical features and imaging features shows promise for identifying pCR in BC. TECHNICAL EFFICACY: Stage 4.

Transfer of miR-877-3p via extracellular vesicles derived from dental pulp stem cells attenuates neuronal apoptosis and facilitates early neurological functional recovery after cerebral ischemia-reperfusion injury through the Bclaf1/P53 signaling pathway.

Cerebral ischemia-reperfusion injury (I/RI) is one of the principal pathogenic factors in the poor prognosis of ischemic stroke, for which current therapeutic options to enhance neurological recovery are notably insufficient. Dental pulp stem cell-derived extracellular vesicles (DPSC-EVs) have promising prospects in stroke treatment and the specific underlying mechanisms have yet to be fully elucidated. The present study observed that DPSC-EVs ameliorated the degree of cerebral edema and infarct volume by reducing the apoptosis of neurons. Furthermore, the miRNA sequencing and functional enrichment analysis identified that miR-877-3p as a key component in DPSC-EVs, contributing to neuroprotection and anti-apoptotic effects. Following target prediction and dual-luciferase assay indicated that miR-877-3p interacted with Bcl-2-associated transcription factor (Bclaf1) to play a function. The miR-877-3p inhibitor or Bclaf1 overexpression reversed the neuroprotective effects of DPSC-EVs. The findings reveal a novel therapeutic pathway where miR-877-3p, transferred via DPSC-EVs, confers neuroprotection against cerebral I/RI, highlighting its potential in promoting neuronal survival and recovery post-ischemia.

Genomic epidemiology reveals multiple mechanisms of linezolid resistance in clinical enterococci in China.

BACKGROUND: Infections caused by linezolid-resistant enterococci (LRE) are clinically difficult to treat and threaten patient health. However, there is a lack of studies on long time-span LRE strains in China. For this reason, our study comprehensively revealed the resistance mechanisms of LRE strains collected in a Chinese tertiary care hospital from 2011 to 2022. METHODS: Enterococcal strains were screened and verified after retrospective analysis of microbial data. Subsequently, 65 LRE strains (61 Enterococcus faecalis and 4 Enterococcus faecium, MIC ≥ 8 µg/ml), 1 linezolid-intermediate Enterococcus faecium (MIC = 4 µg/ml) and 1 linezolid-susceptible Enterococcus faecium (MIC = 1.5 µg/ml) were submitted for whole-genome sequencing (WGS) analysis and bioinformatics analysis. RESULTS: The optrA gene was found to be the most common linezolid resistance mechanism in our study. We identified the wild-type OptrA and various OptrA variants in 98.5% of LRE strains (61 Enterococcus faecalis and 3 Enterococcus faecium). We also found one linezolid-resistant Enterococcus faecium strain carried both optrA and cfr(D) gene, while one linezolid-resistant Enterococcus faecium only harbored the poxtA gene. Most optrA genes (55/64) were located on plasmids, with impB-fexA-optrA, impB-fexA-optrA-erm(A), fexA-optrA-erm(A), and fexA-optrA segments. A minority of optrA genes (9/64) were found on chromosomes with the Tn6674-like platform. Besides, other possible linezolid resistance-associated mechanisms (mutations in the rplC and rplD genes) were also found in 26 enterococcal strains. CONCLUSIONS: Our study suggested that multiple mechanisms of linezolid resistance exist among clinical LRE strains in China.

Dental pulp mesenchymal stem cell-derived exosomes inhibit neuroinflammation and microglial pyroptosis in subarachnoid hemorrhage via the miRNA-197-3p/FOXO3 axis.

BACKGROUND: Subarachnoid hemorrhage (SAH) is a severe stroke subtype that lacks effective treatment. Exosomes derived from human dental pulp stem cells (DPSCs) are a promising acellular therapeutic strategy for neurological diseases. However, the therapeutic effects of DPSC-derived exosomes (DPSC-Exos) on SAH remain unknown. In this study, we investigated the therapeutic effects and mechanisms of action of DPSC-Exos in SAH. MATERIALS AND METHODS: SAH was established using 120 male Sprague-Dawley rats. One hour after SAH induction, DPSC-Exos were administered via tail vein injection. To investigate the effect of DPSC-Exos, SAH grading, short-term and long-term neurobehavioral assessments, brain water content, western blot (WB), immunofluorescence staining, Nissl staining, and HE staining were performed. The role of miR-197-3p/FOXO3 in regulating pyroptosis was demonstrated through miRNA sequencing, bioinformatics analysis, and rescue experiments. The SAH model in vitro was established by stimulating BV2 cells with hemoglobin (Hb) and the underlying mechanism of DPSC-Exos was investigated through WB and Hoechst/PI staining. RESULTS: The expressions of pro-inflammatory cytokines (IL-1ß, IL-6, and TNF-α) were increased after SAH. DPSC-Exos alleviated brain edema and neuroinflammation by inhibiting the expression of FOXO3 and reducing NLRP3 inflammasome activation, leading to improved neurobehavioral functions at 24 h after SAH. In vitro, the expression of the NLRP3 inflammasome components (NLRP3 and caspase1-p20), GSDMD-N, and IL-18 was inhibited in BV2 cells pretreated with DPSC-Exos. Importantly, DPSC-Exos overexpressing miR-197-3p had a more obvious protective effect than those from NC-transfected DPSCs, while those from DPSCs transfected with the miR-197-3p inhibitor had a weaker protective effect. Functional studies indicated that miR-197-3p bound to the 3'-untranslated region of FOXO3, inhibiting its transcription. Furthermore, the overexpression of FOXO3 reversed the protective effects of miR-197-3p. CONCLUSIONS: DPSC-Exos inhibited activation of the NLRP3 inflammasome and related cytokine release via the miR-197-3p/FOXO3 pathway, alleviated neuroinflammation, and inhibited microglial pyroptosis. These findings suggest that using DPSC-Exos is a promising therapeutic strategy for SAH.

A fine-scale map of genome-wide recombination in divergent Escherichia coli population.

Recombination is one of the most important molecular mechanisms of prokaryotic genome evolution, but its exact roles are still in debate. Here we try to infer genome-wide recombination within a species, utilizing a dataset of 149 complete genomes of Escherichia coli from diverse animal hosts and geographic origins, including 45 in-house sequenced with the single-molecular real-time platform. Two major clades identified based on physiological, clinical and ecological characteristics form distinct genetic lineages based on scarcity of interclade gene exchanges. By defining gene-based syntenies for genomic segments within and between the two clades, we build a fine-scale recombination map for this representative global E. coli population. The map suggests extensive within-clade recombination that often breaks physical linkages among individual genes but seldom interrupts the structure of genome organizational frameworks as well as primary metabolic portfolios supported by the framework integrity, possibly due to strong natural selection for both physiological compatibility and ecological fitness. In contrast, the between-clade recombination declines drastically when phylogenetic distance increases to the extent where a 10-fold reduction can be observed, establishing a firm genetic barrier between clades. Our empirical data suggest a critical role for such recombination events in the early stage of speciation where recombination rate is associated with phylogenetic distance in addition to sequence and gene variations. The extensive intraclade recombination binds sister strains into a quasisexual group and optimizes genes or alleles to streamline physiological activities, whereas the sharply declined interclade recombination split the population into clades adaptive to divergent ecological niches.

Metabolic engineering of commensal bacteria for gut butyrate delivery and dissection of host-microbe interaction.

An overwhelming number of studies have reported the correlation of decreased abundance of butyrate-producing commensals with a wide range of diseases. However, the molecular-level mechanisms whereby gut butyrate causally affects the host mucosal immunity and pathogenesis were poorly understood, hindered by the lack of efficient tools to control intestinal butyrate. Here we engineered a facultative anaerobic commensal bacterium to delivery butyrate at the intestinal mucosal surface, and implemented it to dissect the causal role of gut butyrate in regulating host intestinal homeostasis in a model of murine chronic colitis. Mechanistically, we show that gut butyrate protected against colitis and preserved intestinal mucosal homeostasis through its inhibiting effect on the key pyroptosis executioner gasdermin D (GSDMD) of colonic epithelium, via functioning as an HDAC3 inhibitor. Overall, our work presents a new avenue to build synthetic living delivery bacteria to decode causal molecules at the host-microbe interface with molecular-level insights.

Amplicon-based sequencing and co-occurence network analysis reveals notable differences of microbial community structure in healthy and dandruff scalps.

BACKGROUND: Dandruff is a chronic, recurring, and common scalp problem that is caused by several etiopathogeneses with complex mechanisms. Management of this condition is typically achieved via antifungal therapies. However, the precise roles played by microbiota in the development of the condition have not been elucidated. Despite their omnipresence on human scalp little is known about the co-occurrence/co-exclusion network of cutaneous microbiota. RESULTS: We characterized the scalp and hair surface bacterial and fungal communities of 95 dandruff-afflicted and healthy individuals residing in China. The degree distributions of co-occurrence/co-exclusion network in fungi-bacteria and bacteria-bacteria were higher in the healthy group (P < 0.0001), whereas the betweenness values are higher in the dandruff group (P < 0.01). Meanwhile, the co-occurrence/co-exclusion network among fungi-fungi and fungi-bacteria showed that compared to the healthy group, the dandruff group had more positive links (P < 0.0001). In addition, we observed that Malassezia slooffiae, Malassezia japonica and Malassezia furfur, were more abundant in the dandruff group than in the healthy group. These microbiota were co-exclusion by either multiple bacterial genera or Malassezia sp. in healthy group. The lactic acid bacteria on the scalp and hair surface, especially the genera Lactobacillus and Lactococcus, exhibit a negative correlation with multiple bacterial genera on the scalp and hair surface. Lactobacillus plantarum and Pediococcus lactis isolated on the healthy human scalp can inhibit the growth of Staphylococcus epidermidis in vitro. CONCLUSIONS: We showed that microbial networks on scalp and hair surface with dandruff were less integrated than their healthy counterparts, with lower node degree and more positive and stronger links which were deemed to be unstable and may be more susceptible to environmental fluctuations. Lactobacillus bacteria have extensive interactions with other bacteria or fungi in the scalp and hair surface micro-ecological network and can be used as targets for improving scalp health.

A deep learning-machine learning fusion approach for the classification of benign, malignant, and intermediate bone tumors.

OBJECTIVES: To build and validate deep learning and machine learning fusion models to classify benign, malignant, and intermediate bone tumors based on patient clinical characteristics and conventional radiographs of the lesion. METHODS: In this retrospective study, data were collected with pathologically confirmed diagnoses of bone tumors between 2012 and 2019. Deep learning and machine learning fusion models were built to classify tumors as benign, malignant, or intermediate using conventional radiographs of the lesion and potentially relevant clinical data. Five radiologists compared diagnostic performance with and without the model. Diagnostic performance was evaluated using the area under the curve (AUC). RESULTS: A total of 643 patients' (median age, 21 years; interquartile range, 12-38 years; 244 women) 982 radiographs were included. In the test set, the binary category classification task, the radiological model of classification for benign/not benign, malignant/nonmalignant, and intermediate/not intermediate had AUCs of 0.846, 0.827, and 0.820, respectively; the fusion models had an AUC of 0.898, 0.894, and 0.865, respectively. In the three-category classification task, the radiological model achieved a macro average AUC of 0.813, and the fusion model had a macro average AUC of 0.872. In the observation test, the mean macro average AUC of all radiologists was 0.819. With the three-category classification fusion model support, the macro AUC improved by 0.026. CONCLUSION: We built, validated, and tested deep learning and machine learning models that classified bone tumors at a level comparable with that of senior radiologists. Model assistance may somewhat help radiologists' differential diagnoses of bone tumors. KEY POINTS: • The deep learning model can be used to classify benign, malignant, and intermediate bone tumors. • The machine learning model fusing information from radiographs and clinical characteristics can improve the classification capacity for bone tumors. • The diagnostic performance of the fusion model is comparable with that of senior radiologists and is potentially useful as a complement to radiologists in a bone tumor differential diagnosis.

The Dissemination of NDM-1 in Acinetobacter baumannii Strains.

Acinetobacter baumannii is a common pathogen in hospitals and usually causes bacteremia, pneumonia, meningitis, peritonitis and other diseases. Isolates carried NDM-1 gene can make several antibiotics such as carbapenems and other beta-lactams ineffective. Nowadays, the number of A. baumannii strains carrying NDM-1 has been climbing year by year in recent years. To characterise the transmission of NDM-1 in A. baumannii, we collected 2576 human-derived genomes of A. baumannii strains from NCBI database and found that 186 strains contained NDM-1 gene. The multi-locus sequence typing, phylogenetic tree, NDM-1 gene organization and the single nucleotide polymorphisms of NDM-1 were investigated. We hope that our work will provide a theoretical basis for the prevention of dissemination of NDM-1 in A. baumannii.

OXPHOS-dependent metabolic reprogramming prompts metastatic potential of breast cancer cells under osteogenic differentiation.

BACKGROUND: Microcalcification is one of the most reliable clinical features of the malignancy risk of breast cancer, and it is associated with enhanced tumour aggressiveness and poor prognosis. However, its underlying molecular mechanism remains unclear. METHODS: Clinical data were retrieved to analyse the association between calcification and bone metastasis in patients with breast cancer. Using multiple human breast cancer cell lines, the osteogenic cocktail model was established in vitro to demonstrate calcification-exacerbated metastasis. Migration and invasion characteristics were determined by wound healing and transwell migration. mRNA and protein expression were identified by quantitative PCR and western blotting. Metabolic alterations in breast cancer cells were evaluated using Seahorse Analyser. RESULTS: The osteogenic differentiation of human breast cancer cells activated the classical TGF-ß/Smad signalling pathway and the non-canonical MAPK pathway, which, in turn, exacerbated the progression of epithelial-mesenchymal transition (EMT). The metabolic programme switched to enhancing mitochondrial oxidative phosphorylation (OXPHOS) upon osteogenic differentiation. Rotenone was used to inhibit the OXPHOS complex during osteogenesis to block mitochondrial function, consequently reversing the EMT phenotype. CONCLUSIONS: This study provides important insights into the mechanisms involved in breast cancer bone metastasis, and outlines a possible strategy to intervene in OXPHOS for the treatment of breast tumours.

Digital breast tomosynthesis versus digital mammography: integration of image modalities enhances deep learning-based breast mass classification.

OBJECTIVE: To evaluate the impact of utilizing digital breast tomosynthesis (DBT) or/and full-field digital mammography (FFDM), and different transfer learning strategies on deep convolutional neural network (DCNN)-based mass classification for breast cancer. METHODS: We retrospectively collected 441 patients with both DBT and FFDM on which regions of interest (ROIs) covering the malignant, benign and normal tissues were extracted for DCNN training and validation. Experiments were conducted for tasks in distinguishing malignant/benign/normal: (1) classification capabilities of DBT vs FFDM and the role of transfer learning were validated on 2D-DCNN; (2) different strategies of combining DBT and FFDM and the associated impacts on classification were explored; (3) 2D-DCNN and 3D-DCNN trained from scratch with volumetric DBT were compared. RESULTS: 2D-DCNN with transfer learning outperformed that without for DBT in distinguishing malignant (ΔAUC = 0.059 ± 0.009, p < 0.001), benign (ΔAUC = 0.095 ± 0.010, p < 0.001) and normal tissue (ΔAUC = 0.042 ± 0.004, p < 0.001) (paired samples t test). 2D-DCNN trained on DBT (with transfer learning) achieved higher accuracy than those on FFDM (malignant: ΔAUC = 0.014 ± 0.014, p = 0.037; benign: ΔAUC = 0.031 ± 0.006, p < 0.001; normal: ΔAUC = 0.017 ± 0.004, p < 0.001) (independent samples t test). The 2D-DCNN employing both DBT and FFDM for training achieved better performances in benign (FFDM: ΔAUC = 0.010 ± 0.008, p < 0.001; DBT: ΔAUC = 0.009 ± 0.005, p < 0.001) and normal (FFDM: ΔAUC = 0.005 ± 0.003, p < 0.001; DBT: ΔAUC = 0.002 ± 0.002, p < 0.001) (related samples Friedman test). The 3D-DCNN and 2D-DCNN trained from scratch with DBT only produced moderate classification. CONCLUSIONS: Transfer learning facilitates mass classification for both DBT and FFDM, and DBT outperforms FFDM when equipped with transfer learning. Integrating DBT and FFDM in DCNN training enhances mass classification accuracy for breast cancer. KEY POINTS: • Transfer learning facilitates mass classification for both DBT and FFDM, and the DBT-based DCNN outperforms the FFDM-based DCNN when equipped with transfer learning. • Integrating DBT and FFDM in DCNN training enhances breast mass classification accuracy. • 3D-DCNN/2D-DCNN trained from scratch with volumetric DBT but without transfer learning only produce moderate mass classification result.

Semi-rational mutagenesis of an industrial Streptomyces fungicidicus strain for improved enduracidin productivity.

The biosynthesis of the valuable antibiotic enduracidin by Streptomyces fungicidicus TXX3120 is a complex multistep process. To identify the rate-limiting step of the entire biosynthetic process, we carried out a deep RNA sequencing towards the mycelia of TXX3120 at different fermentation stages. Comparative RNA-seq analysis indicated that the expression level of the endC gene during the enduracidin production phase was evidently lower than that of the other relevant genes to enduracidin biosynthesis. This result was further confirmed by quantitative RT-PCR, and the giant non-ribosomal peptide synthase (NRPS) encoded by endC was predicated to be the rate-limiting enzyme in enduracidin biosynthesis. To increase the expression of endC during the enduracidin production phase, a reporter-based selection system was developed by genetically replacing the initial part of the endC gene with a thiostrepton resistance gene (tsr), which will then act as a selectable marker to report the expression level of the rate-limiting gene endC, thereby facilitating the selection of enduracidin-overproducing mutants following random mutagenesis. After one round of mutagenesis, thiostrepton resistance selection, and restoration of the endC gene, three mutant strains with improved endC expression levels were obtained. Their highest enduracidin titers reached 9780.54, 9272.46, and 8849.06 U/mL, respectively representing 2.31-, 2.19-, and 2.09-fold of the initial industrial strain TXX3120. Our research provides a useful strategy for the rational breeding of industrial strains that synthesize complex natural products.

Transcriptomic profiling reveals MEP pathway contributing to ginsenoside biosynthesis in Panax ginseng.

BACKGROUND: Panax ginseng C. A. Mey is one of famous medicinal herb plant species. Its major bioactive compounds are various ginsenosides in roots and rhizomes. It is commonly accepted that ginsenosides are synthesized from terpene precursors, IPP and DMAPP, through the cytoplasmic mevalonate (MVA) pathway. Another plastic 2-C-methyl-D-erythritol 4-phosphate (MEP) pathway was proved also contributing to ginsenoside generation in the roots of P. ginseng by using specific chemical inhibitors recently. But their gene expression characteristics are still under reveal in P. ginseng. With the development of the high-throughput next generation sequencing (NGS) technologies, we have opportunities to discover more about the complex ginsenoside biosynthesis pathways in P. ginseng. RESULTS: We carried out deep RNA sequencing and comprehensive analyses on the ginseng root samples of 1-5 years old and five different tissues of 5 years old ginseng plants. The de novo assembly totally generated 48,165 unigenes, including 380 genes related to ginsenoside biosynthesis and all the genes encoding the enzymes of the MEP pathway and the MVA pathway. We further illustrated the gene expression profiles related to ginsenoside biosynthesis among 1-5 year-old roots and different tissues of 5 year-old ginseng plants. Particularly for the first time, we revealed that the gene transcript abundances of the MEP pathway were similar to those of the MVA pathway in ginseng roots but higher in ginseng leaves. The IspD was predicated to be the rate-limiting enzyme in the MEP pathway through both co-expression network and gene expression profile analyses. CONCLUSIONS: At the transcriptional level, the MEP pathway has similar contribution to ginsenoside biosynthesis in ginseng roots, but much higher in ginseng leaves, compared with the MVA pathway. The IspD might be the key enzyme for ginsenoside generation through the MEP pathway. These results provide new information for further synthetic biology study on ginsenoside metabolic regulation.

Evolview v2: an online visualization and management tool for customized and annotated phylogenetic trees.

Evolview is an online visualization and management tool for customized and annotated phylogenetic trees. It allows users to visualize phylogenetic trees in various formats, customize the trees through built-in functions and user-supplied datasets and export the customization results to publication-ready figures. Its 'dataset system' contains not only the data to be visualized on the tree, but also 'modifiers' that control various aspects of the graphical annotation. Evolview is a single-page application (like Gmail); its carefully designed interface allows users to upload, visualize, manipulate and manage trees and datasets all in a single webpage. Developments since the last public release include a modern dataset editor with keyword highlighting functionality, seven newly added types of annotation datasets, collaboration support that allows users to share their trees and datasets and various improvements of the web interface and performance. In addition, we included eleven new 'Demo' trees to demonstrate the basic functionalities of Evolview, and five new 'Showcase' trees inspired by publications to showcase the power of Evolview in producing publication-ready figures. Evolview is freely available at: http://www.evolgenius.info/evolview/.

[Identification of effective components from Naoxintong Capsules intestinal absorption liquid in inhibiting ADP-induced platelet aggregation].

Intestinal absorption liquid was prepared by using everted intestinal sac method; meanwhile, its recipes were decomposed or restructured. Platelet aggregation activity was examined by biochemical tests and a microplate reader. One or more kinds of Chinese medicines which displayed inhibiting activity in Naoxintong Capsules were screened through separation and combination of prescription. The results showed that Naoxintong Capsules could inhibit ADP-induced platelet aggregation. Recipe decomposition and restructuring results showed that Salviae Miltiorrhizae Radix et Rhizoma, Paeoniae Radix Rubra, Cinnamomi Ramulus and Hirudo were the main effective medicines in inhibiting platelet aggregation. Furthermore, Cinnamomi Ramulus played a vital role in inhibiting activity among those four kinds of Chinese medicines. Coumarin derived from intestinal absorption liquid of Cinnamomi Ramulus had inhibiting activity in the range of 50-200 µmol·L⁻¹, and other ingredients such as cinnamyl alcohol and cinnamaldehyde also had inhibiting activities. In conclusion, Salviae Miltiorrhizae Radix et Rhizoma, Paeoniae Radix Rubra, Cinnamomi Ramulus and Hirudo are the main components for inhibiting ADP-induced platelet aggregation, and Cinnamomi Ramulus has the most strongest inhibiting activity in Naoxintong Capsules.

Complete nucleotide sequence of pH11, an IncHI2 plasmid conferring multi-antibiotic resistance and multi-heavy metal resistance genes in a clinical Klebsiella pneumoniae isolate.

The complete 284,628bp sequence of pH11, an IncHI2 plasmid, was determined through single-molecule, real-time (SMRT) sequencing. Harbored by a clinical Klebsiella pneumoniae strain H11, and isolated in Beijing, this plasmid contains multiple antibiotic resistance genes, including catA2, aac(6')-Ib, strB, strA, dfrA19, blaTEM-1, blaSHV-12, sul1, qacE delta 1, ereA, arr2, and aac3. The aac(6')-Ib is carried by a class I integron. Plasmid pH11 also carries several genes associated with resistance to heavy metals, such as tellurium, mercury, cobalt, zinc, nickel, copper, lead and cadmium. This plasmid exhibits numerous characteristics, including HipBA and RelBE toxin-antitoxin systems, two major transfer (Tra) regions closely related to those of Salmonella enterica serovar plasmid pRH-R27, a type II restriction modification system (EcoRII R-M system), several methyltransferases and methylases and genes encoding Hha and StpA. These characteristics suggest that pH11 may adapt to various hosts and environments. Multiple insertion sequence elements, transposases, recombinases, resolvases and integrases are scattered throughout pH11. The presence of these genes may indicate that horizontal gene transfer occurs frequently in pH11 and thus may facilitate the dissemination of antimicrobial resistance determinants. Our data suggest that pH11 is a chimera gradually assembled through the integration of different horizontally acquired DNA segments via transposition or homologous recombination.

An integrated RNA-Seq and network study reveals a complex regulation process of rice embryo during seed germination.

Seed germination is a crucial stage for plant development and agricultural production. To investigate its complex regulation process, the RNA-Seq study of rice embryo was conducted at three time points of 0, 12 and 48 h post imbibition (HPI). Dynamic transcriptional alterations were observed, especially in the early stage (0-12 HPI). Seed related genes, especially those encoding desiccation inducible proteins and storage reserves in embryo, decreased drastically after imbibition. The expression profiles of phytohormone related genes indicated distinct roles of abscisic acid (ABA), gibberellin (GA) and brassinosteroid (BR) in germination. Moreover, network analysis revealed the importance of protein phosphorylation in phytohormone interactions. Network and gene ontology (GO) analyses suggested that transcription factors (TFs) played a regulatory role in functional transitions during germination, and the enriched TF families at 0 HPI implied a regulation of epigenetic modification in dry seeds. In addition, 35 germination-specific TF genes in embryo were identified and seven genes were verified by qRT-PCR. Besides, enriched TF binding sites (TFBSs) supported physiological changes in germination. Overall, this study expands our comprehensive knowledge of multiple regulation factors underlying rice seed germination.

High Expression of CLDN 18.2 is Associated with Poor Disease-Free Survival of HER-2 Positive Gastric Cancer.

Background: Anti-claudin (CLDN) 18.2 therapy has been proven to be effective in treating advanced gastric cancer with negative human epidermal growth factor receptor 2 (HER-2). This study purposed to investigate the relationship of CLDN 18.2 expression with prognosis of HER-2-positive gastric cancer patients. Objective: To investigate the expression of claudin (CLDN) 18.2 in Human epidermal growth factor receptor 2 (HER-2) positive gastric cancer patients after radical resection and its relationship with gastric cancer prognosis. Methods: A total of 55 postoperative HER-2-positive gastric cancer patients were included in this study. CLDN 18.2 protein was detected by immunohistochemistry, and detailed clinical and pathological information was collected. Factors considered potentially important in the univariate analysis were included in the multivariate analysis, which involved COX regression to find the independent prognostic factors affecting disease-free survival (DFS). Results: Immunohistochemistry showed that different levels of CLDN 18.2 protein were expressed in HER-2 positive gastric cancer tissues, and the Chi-square analysis showed that the expression level of CLDN 18.2 was significantly correlated with the lymph node stage. Higher expression levels of CLDN 18.2 were found in patients with lymph node positivity and were associated with poor prognosis in HER-2-positive gastric cancer patients. Gastric cancer patients with low and high expressions of CLDN 18.2 had postoperative median DFS of 38.5 months (95% confidence interval (CI) 28.8-48.2 months) and 12.1 months (95% CI, 11.7-41.0 months), respectively. Conclusion: High expression of CLDN 18.2 in HER-2 positive gastric cancer is associated with poor prognosis, and the optimal treatment mode for this population is worth exploring after the approval of anti-CLDN 18.2 drugs.

5-Fluorouracil resistant CRC cells derived exosomes promote cancer-associated fibroblasts secreting more CXCL12.

Background: Chemoresistance is a key reason for treatment failure in colorectal cancer (CRC) patients. The tumor microenvironment of chemoresistant CRC is distinctly immunosuppressive, although the underlying mechanisms are unclear. Methods: The CRC data sets GSE69657 and GSE62080 were downloaded from the GEO database, and the correlation between TRPC5 and FAP expression was analyzed by Pearson method. The in-situ expression of transient receptor potential channel 5 (TRPC5) and fibroblast activation protein (FAP) in the CRC tissues was examined by immunohistochemistry. TRPC5 expression levels in the HCT8 and HCT116 cell lines and the corresponding 5-fluorouracil (5-FU)-resistant cell lines (HCT8R and HCT116R) were analyzed by western blotting and RT-PCR. Exosomes were isolated from the HCT8R and HCT116R cells and incubated with colorectal normal fibroblasts (NFs), and cancer-associated fibroblasts (CAFs)markers were detected. NFs were also incubated with exosomes isolated from TRPC5-knockdown HCT8R cells, and the changes in intracellular Ca2+ levels and C-X-C motif chemokine ligand 12 (CXCL12) secretion were analyzed. Results: TRPC5 and FAP expression showed positive correlation in the datasets. Immunostaining of CRC tissue specimens further revealed that high TRPC5 and FAP expressions were significantly associated with worse tumor regression. Furthermore, chemoresistant CRC cells expressed higher levels of TRPC5 compared to the chemosensitive cells, and knocking down TRPC5 reversed chemoresistance. Exosomes derived from CRC cells induced the transformation of NFs to CAFs. However, TRPC5-exosomes derived from chemoresistant CRC cells can promote CAFs to secrete more CXCL12. Conclusion: Chemoresistant CRC cells can induce CAFs activation and promote CXCL12 secretion through exosomal TRPC5.